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Introduction to Python for Kids: Learn Python the Fun Way by Completing Activities and Solving Puzzles
by Aarthi ElumalaiWant to create cool games and apps to impress your friends (or yourself), but not sure where to start? Or, have you tried your hand at programming, but got utterly bored after combing through hundreds of pages of dry text? Then you’ve come to the right place! This book is the perfect blend of education and fun for kids 8 years and above looking to learn the magic of Python, one of the easiest and most powerful programming languages around, all while solving fun puzzles and building your own projects on the way. Yes, there’ll be chapters on the fundamentals of Python, such as variables, numbers, strings, automation with conditions, loops and functions, objects, and files. But, early on in the book you’ll get started with Turtle, a Python package that was custom-made for kids like you. It lets you literally draw and animate on your computer! Every concept will be interspersed with a fun mini project with Turtle, so you’ll never get bored. Once you get the fundamentals down, you’ll dive right into Tkinter and Pygame, more fun Python packages (goodbye theory!) and you’ll learn all about creating apps and games like the ones you see and use every day (bouncing ball, temperature converter, calculator, rock-paper-scissors, and so much more!). There are also four capstone projects at the end of the book that convert everything you’ve learned so far into full-blown apps and games that you can show off to your friends, parents, and even teachers! You’ll be creating a snake game with Turtle, a tic tac toe game with Tkinter, a full-fledged paint app, again with Tkinter, and finally, a classic space shooters game with Pygame (the cherry on top). Every project chapter will be accompanied with the logic behind the game/app and an explanation on how you’ve arrived at the logic. You’ll develop strong problem solving skills that’ll help you create future projects on your own. There are also two chapters dedicated to just creating fun mini projects and puzzles, one of them placed in the middle of the book to give you a welcome break from all the learning. The book ends with an overview on web development with Python and ideas for more fun projects and puzzles you can solve on your own. Become the “most likely to succeed” kid in your grade while having the most fun getting there! What You'll Learn Gain a gentle, but thorough introduction into the world of programming and Python Create programs and solve problems with core Python concepts Build mini projects and capstone projects (showcase worthy) with Turtle, Tkinter, and Pygame Develop programming skills while doing the puzzles and activities described in the book Who This Book Is ForKids 8 years and above.
Introduction to Python for Science and Engineering (ISSN)
by David J. PineIntroduction to Python for Science and Engineering offers a quick and incisive introduction to the Python programming language for use in any science or engineering discipline. The approach is pedagogical and “bottom up,” which means starting with examples and extracting more general principles from that experience. No prior programming experience is assumed.Readers will learn the basics of Python syntax, data structures, input and output, conditionals and loops, user-defined functions, plotting, animation, and visualization. They will also learn how to use Python for numerical analysis, including curve fitting, random numbers, linear algebra, solutions to nonlinear equations, numerical integration, solutions to differential equations, and fast Fourier transforms.Readers learn how to interact and program with Python using JupyterLab and Spyder, two simple and widely used integrated development environments.All the major Python libraries for science and engineering are covered, including NumPy, SciPy, Matplotlib, and Pandas. Other packages are also introduced, including Numba, which can render Python numerical calculations as fast as compiled computer languages such as C but without their complex overhead.
Introduction to Python for Science and Engineering (Series in Computational Physics)
by David PineThis guide offers a quick and incisive introduction to Python programming for anyone. The author has carefully developed a concise approach to using Python in any discipline of science and engineering, with plenty of examples, practical hints, and insider tips. <p><p>Readers will see why Python is such a widely appealing program, and learn the basics of syntax, data structures, input and output, plotting, conditionals and loops, user-defined functions, curve fitting, numerical routines, animation, and visualization. The author teaches by example and assumes no programming background for the reader. <p><p>David J. Pine is the Silver Professor and Professor of Physics at New York University, and Chair of the Department of Chemical and Biomolecular Engineering at the NYU Tandon School of Engineering. He is an elected fellow of the American Physical Society and American Association for the Advancement of Science (AAAS), and is a Guggenheim Fellow.
Introduction to Python in Earth Science Data Analysis: From Descriptive Statistics to Machine Learning (Springer Textbooks in Earth Sciences, Geography and Environment)
by Maurizio PetrelliThis textbook introduces the use of Python programming for exploring and modelling data in the field of Earth Sciences. It drives the reader from his very first steps with Python, like setting up the environment and starting writing the first lines of codes, to proficient use in visualizing, analyzing, and modelling data in the field of Earth Science. Each chapter contains explicative examples of code, and each script is commented in detail. The book is minded for very beginners in Python programming, and it can be used in teaching courses at master or PhD levels. Also, Early careers and experienced researchers who would like to start learning Python programming for the solution of geological problems will benefit the reading of the book.
Introduction to Python: With Applications in Optimization, Image and Video Processing, and Machine Learning (Chapman & Hall/CRC The Python Series)
by David Báez-López David Alfredo Báez VillegasIntroduction to Python: with Applications in Optimization, Image and Video Processing, and Machine Learning is intended primarily for advanced undergraduate and graduate students in quantitative sciences such as mathematics, computer science, and engineering. In addition to this, the book is written in such a way that it can also serve as a self-contained handbook for professionals working in quantitative fields including finance, IT, and many other industries where programming is a useful or essential tool.The book is written to be accessible and useful to those with no prior experience of Python, but those who are somewhat more adept will also benefit from the more advanced material that comes later in the book.Features Covers introductory and advanced material. Advanced material includes lists, dictionaries, tuples, arrays, plotting using Matplotlib, object-oriented programming Suitable as a textbook for advanced undergraduates or postgraduates, or as a reference for researchers and professionals Solutions manual, code, and additional examples are available for download
Introduction to Quantitative Social Science with Python (Chapman & Hall/CRC The Python Series)
by Weiqi Zhang Dmitry ZinovievDeparting from traditional methodologies of teaching data analysis, this book presents a dual-track learning experience, with both Executive and Technical Tracks, designed to accommodate readers with various learning goals or skill levels. Through integrated content, readers can explore fundamental concepts in data analysis while gaining hands-on experience with Python programming, ensuring a holistic understanding of theory and practical application in Python.Emphasizing the practical relevance of data analysis in today's world, the book equips readers with essential skills for success in the field. By advocating for the use of Python, an open-source and versatile programming language, we break down financial barriers and empower a diverse range of learners to access the tools they need to excel.Whether you're a novice seeking to grasp the foundational concepts of data analysis or a seasoned professional looking to enhance your programming skills, this book offers a comprehensive and accessible guide to mastering the art and science of data analysis in social science research.Key Features: Dual-track learning: Offers both Executive and Technical Tracks, catering to readers with varying levels of conceptual and technical proficiency in data analysis. Includes comprehensive quantitative methodologies for quantitative social science studies. Seamless integration: Interconnects key concepts between tracks, ensuring a smooth transition from theory to practical implementation for a comprehensive learning experience. Emphasis on Python: Focuses on Python programming language, leveraging its accessibility, versatility, and extensive online support to equip readers with valuable data analysis skills applicable across diverse domains.
Introduction to Quantum Algorithms via Linear Algebra, second edition
by Richard J. Lipton Kenneth W. ReganQuantum computing explained in terms of elementary linear algebra, emphasizing computation and algorithms and requiring no background in physics.This introduction to quantum algorithms is concise but comprehensive, covering many key algorithms. It is mathematically rigorous but requires minimal background and assumes no knowledge of quantum theory or quantum mechanics. The book explains quantum computation in terms of elementary linear algebra; it assumes the reader will have some familiarity with vectors, matrices, and their basic properties, but offers a review of the relevant material from linear algebra. By emphasizing computation and algorithms rather than physics, it makes quantum algorithms accessible to students and researchers in computer science who have not taken courses in quantum physics or delved into fine details of quantum effects, apparatus, circuits, or theory.
Introduction to Quantum Computing (The Materials Research Society Series)
by Ray LaPierreThis book provides a self-contained undergraduate course on quantum computing based on classroom-tested lecture notes. It reviews the fundamentals of quantum mechanics from the double-slit experiment to entanglement, before progressing to the basics of qubits, quantum gates, quantum circuits, quantum key distribution, and some of the famous quantum algorithms. As well as covering quantum gates in depth, it also describes promising platforms for their physical implementation, along with error correction, and topological quantum computing. With quantum computing expanding rapidly in the private sector, understanding quantum computing has never been so important for graduates entering the workplace or PhD programs. Assuming minimal background knowledge, this book is highly accessible, with rigorous step-by-step explanations of the principles behind quantum computation, further reading, and end-of-chapter exercises, ensuring that undergraduate students in physics and engineering emerge well prepared for the future.
Introduction to Quantum Computing: From a Layperson to a Programmer in 30 Steps
by Hiu Yung WongThis textbook introduces quantum computing to readers who do not have much background in linear algebra. The author targets undergraduate and master students, as well as non-CS and non-EE students who are willing to spend about 60 -90 hours seriously learning quantum computing. Readers will be able to write their program to simulate quantum computing algorithms and run on real quantum computers on IBM-Q. Moreover, unlike the books that only give superficial, “hand-waving” explanations, this book uses exact formalism so readers can continue to pursue more advanced topics based on what they learn from this book.Encourages students to embrace uncertainty over the daily classical experience, when encountering quantum phenomena;Uses narrative to start each section with analogies that help students to grasp the critical concept quickly;Uses numerical substitutions, accompanied by Python programming and IBM-Q quantum computer programming, as examples in teaching all critical concepts.
Introduction to Quantum Computing: From a Layperson to a Programmer in 30 Steps
by Hiu Yung WongThis textbook introduces quantum computing to readers who do not have much background in linear algebra based on the self-study experience of the author as an engineer. The author targets undergraduate and master students who are willing to spend about 60 -90 hours seriously learning quantum computing. This book is also suitable for self-study and teaching videos for each chapter and more than 200 exercises with answers are provided. Readers will be able to write their program to simulate quantum computing algorithms and run on real quantum computers on IBM-Q. Moreover, unlike books that only give superficial, “hand-waving” explanations, this book uses exact formalism so readers can continue to pursue more advanced topics based on what they learn from this book
Introduction to Quantum Information Science
by Masahito Hayashi Satoshi Ishizaka Akinori Kawachi Gen Kimura Tomohiro OgawaThis book presents the basics of quantum information, e. g. , foundation of quantum theory, quantum algorithms, quantum entanglement, quantum entropies, quantum coding, quantum error correction and quantum cryptography. The required knowledge is only elementary calculus and linear algebra. This way the book can be understood by undergraduate students. In order to study quantum information, one usually has to study the foundation of quantum theory. This book describes it from more an operational viewpoint which is suitable for quantum information while traditional textbooks of quantum theory lack this viewpoint. The current book bases on Shor's algorithm, Grover's algorithm, Deutsch-Jozsa's algorithm as basic algorithms. To treat several topics in quantum information, this book covers several kinds of information quantities in quantum systems including von Neumann entropy. The limits of several kinds of quantum information processing are given. As important quantum protocols, this book contains quantum teleportation, quantum dense coding, quantum data compression. In particular conversion theory of entanglement via local operation and classical communication are treated too. This theory provides the quantification of entanglement, which coincides with von Neumann entropy. The next part treats the quantum hypothesis testing. The decision problem of two candidates of the unknown state are given. The asymptotic performance of this problem is characterized by information quantities. Using this result, the optimal performance of classical information transmission via noisy quantum channel is derived. Quantum information transmission via noisy quantum channel by quantum error correction are discussed too. Based on this topic, the secure quantum communication is explained. In particular, the quantification of quantum security which has not been treated in existing book is explained. This book treats quantum cryptography from a more practical viewpoint.
Introduction to Queueing Networks: Theory ∩ Practice (Springer Series in Operations Research and Financial Engineering)
by J. MacGregor SmithThe book examines the performance and optimization of systems where queueing and congestion are important constructs. Both finite and infinite queueing systems are examined. Many examples and case studies are utilized to indicate the breadth and depth of the queueing systems and their range of applicability. Blocking of these processes is very important and the book shows how to deal with this problem in an effective way and not only compute the performance measures of throughput, cycle times, and WIP but also to optimize the resources within these systems. The book is aimed at advanced undergraduate, graduate, and professionals and academics interested in network design, queueing performance models and their optimization. It assumes that the audience is fairly sophisticated in their mathematical understanding, although the explanations of the topics within the book are fairly detailed.
Introduction to Queueing Systems with Telecommunication Applications
by Laszlo Lakatos Miklos Telek Laszlo SzeidlThe book is composed of two main parts: mathematical background and queueing systems with applications. The mathematical background is a self containing introduction to the stochastic processes of the later studies queueing systems. It starts with a quick introduction to probability theory and stochastic processes and continues with chapters on Markov chains and regenerative processes. More recent advances of queueing systems are based on phase type distributions, Markov arrival processes and quasy birth death processes, which are introduced in the last chapter of the first part. The second part is devoted to queueing models and their applications. After the introduction of the basic Markovian (from M/M/1 to M/M/1//N) and non-Markovian (M/G/1, G/M/1) queueing systems, a chapter presents the analysis of queues with phase type distributions, Markov arrival processes (from PH/M/1 to MAP/PH/1/K). The next chapter presents the classical queueing network results and the rest of this part is devoted to the application examples. There are queueing models for bandwidth charing with different traffic classes, slotted multiplexers, ATM switches, media access protocols like Aloha and IEEE 802.11b, priority systems and retrial systems. An appendix supplements the technical content with Laplace and z transformation rules, Bessel functions and a list of notations. The book contains examples and exercises throughout and could be used for graduate students in engineering, mathematics and sciences.
Introduction to Queueing Systems with Telecommunication Applications
by László Lakatos László Szeidl Miklós TelekThe book is the extended and revised version of the 1st edition and is composed of two main parts: mathematical background and queueing systems with applications. The mathematical background is a self-containing introduction to the stochastic processes of the later studied queueing systems. It starts with a quick introduction to probability theory and stochastic processes and continues with chapters on Markov chains and regenerative processes. More recent advances of queueing systems are based on phase type distributions, Markov arrival processes and quasy birth death processes, which are introduced in the last chapter of the first part.The second part is devoted to queueing models and their applications. After the introduction of the basic Markovian (from M/M/1 to M/M/1//N) and non-Markovian (M/G/1, G/M/1) queueing systems, a chapter presents the analysis of queues with phase type distributions, Markov arrival processes (from PH/M/1 to MAP/PH/1/K). The next chapter presents the classical queueing network results and the rest of this part is devoted to the application examples. There are queueing models for bandwidth charing with different traffic classes, slotted multiplexers, media access protocols like Aloha and IEEE 802.11b, priority systems and retrial systems.An appendix supplements the technical content with Laplace and z transformation rules, Bessel functions and a list of notations. The book contains examples and exercises throughout and could be used for graduate students in engineering, mathematics and sciences.Reviews of first edition:"The organization of the book is such that queueing models are viewed as special cases of more general stochastic processes, such as birth-death or semi-Markov processes. … this book is a valuable addition to the queuing literature and provides instructors with a viable alternative for a textbook to be used in a one- or two-semester course on queueing models, at the upper undergraduate or beginning graduate levels."Charles Knessl, SIAM Review, Vol. 56 (1), March, 2014
Introduction to R for Business Intelligence
by Jay GendronLearn how to leverage the power of R for Business Intelligence About This Book * Use this easy-to-follow guide to leverage the power of R analytics and make your business data more insightful. * This highly practical guide teaches you how to develop dashboards that help you make informed decisions using R. * Learn the A to Z of working with data for Business Intelligence with the help of this comprehensive guide. Who This Book Is For This book is for data analysts, business analysts, data science professionals or anyone who wants to learn analytic approaches to business problems. Basic familiarity with R is expected. What You Will Learn * Extract, clean, and transform data * Validate the quality of the data and variables in datasets * Learn exploratory data analysis * Build regression models * Implement popular data-mining algorithms * Visualize results using popular graphs * Publish the results as a dashboard through Interactive Web Application frameworks In Detail Explore the world of Business Intelligence through the eyes of an analyst working in a successful and growing company. Learn R through use cases supporting different functions within that company. This book provides data-driven and analytically focused approaches to help you answer questions in operations, marketing, and finance. In Part 1, you will learn about extracting data from different sources, cleaning that data, and exploring its structure. In Part 2, you will explore predictive models and cluster analysis for Business Intelligence and analyze financial times series. Finally, in Part 3, you will learn to communicate results with sharp visualizations and interactive, web-based dashboards. After completing the use cases, you will be able to work with business data in the R programming environment and realize how data science helps make informed decisions and develops business strategy. Along the way, you will find helpful tips about R and Business Intelligence. Style and approach This book will take a step-by-step approach and instruct you in how you can achieve Business Intelligence from scratch using R. We will start with extracting data and then move towards exploring, analyzing, and visualizing it. Eventually, you will learn how to create insightful dashboards that help you make informed decisions--and all of this with the help of real-life examples.
Introduction to R for Quantitative Finance
by Daniel Havran Michael Puhle Peter Csoka Edina Berlinger Gergely DarocziThis book is a tutorial guide for new users that aims to help you understand the basics of and become accomplished with the use of R for quantitative finance.If you are looking to use R to solve problems in quantitative finance, then this book is for you. A basic knowledge of financial theory is assumed, but familiarity with R is not required. With a focus on using R to solve a wide range of issues, this book provides useful content for both the R beginner and more experience users.
Introduction to R for Terrestrial Ecology: Basics of Numerical Analysis, Mapping, Statistical Tests and Advanced Application of R
by Keith M. Reynolds Milena Lakicevic Nicholas PovakThis textbook covers R data analysis related to environmental science, starting with basic examples and proceeding up to advanced applications of the R programming language. The main objective of the textbook is to serve as a guide for undergraduate students, who have no previous experience with R, but part of the textbook is dedicated to advanced R applications, and will also be useful for Masters and PhD students, and professionals. The textbook deals with solving specific programming tasks in R, and tasks are organized in terms of gradually increasing R proficiency, with examples getting more challenging as the chapters progress. The main competencies students will acquire from this textbook are: manipulating and processing data tablesperforming statistical testscreating maps in R This textbook will be useful in undergraduate and graduate courses in Advanced Landscape Ecology, Analysis of Ecological and Environmental Data, Ecological Modeling, Analytical Methods for Ecologists, Statistical Inference for Applied Research, Elements of Statistical Methods, Computational Ecology, Landscape Metrics and Spatial Statistics.
Introduction to Radar Analysis (Advances in Applied Mathematics)
by Bassem R. MahafzaIntroduction to Radar Analysis, Second Edition is a major revision of the popular textbook. It is written within the context of communication theory as well as the theory of signals and noise. By emphasizing principles and fundamentals, the textbook serves as a vital source for students and engineers. Part I bridges the gap between communication, signal analysis, and radar. Topics include modulation techniques and associated Continuous Wave (CW) and pulsed radar systems. Part II is devoted to radar signal processing and pulse compression techniques. Part III presents special topics in radar systems including radar detection, radar clutter, target tracking, phased arrays, and Synthetic Aperture Radar (SAR). Many new exercise are included and the author provides comprehensive easy-to-follow mathematical derivations of all key equations and formulas. The author has worked extensively for the U.S. Army, the U.S. Space and Missile Command, and other military agencies. This is not just a textbook for senior level and graduates students, but a valuable tool for practicing radar engineers. Features Authored by a leading industry radar professional. Comprehensive up-to-date coverage of radar systems analysis issues. Easy to follow mathematical derivations of all equations and formulas Numerous graphical plots and table format outputs. One part of the book is dedicated to radar waveforms and radar signal processing.
Introduction to React
by Cory GackenheimerIntroduction to React teaches you React, the JavaScript framework created by developers at Facebook, to solve the problem of building complex user interfaces in a consistent and maintainable way. React. js shrugs away common front-end conventions in an effort to make things more efficient - use Introduction to React to learn about this framework and more today. Get to know the React API and it's specific JavaScript extension, JSX, which makes authoring React components easier and maintainable. You will also learn how to test your React applications and about the tools you can use while building. Once you understand these core concepts, you can build applications with React. This will help you cement the ideas and fundamentals of React and prepare you to utilize React in your own use case. What you'll learn How to use React to maintain complex user interfaces in an efficient way How to integrate existing user interfaces and move forward with React How to manage application architecture using Flux How to easily utilize JSX, React's JavaScript extension Who this book is for Introduction to React is for a web developer who is comfortable writing JavaScript and CSS. You will apply JavaScript to build web pages that utilize the paradigm shifting React framework. Introduction to React will provide you with the tools to create maintainable complex user interfaces. Table of Contents Chapter 1: What is React? Chapter 2: The Core of React Chapter 3: JSX Fundamentals Chapter 4: Building A React Web Application Chapter 5: Introducing Flux: An Application Architecture for React Chapter 6: Using Flux to Structure a React Application
Introduction to Recursive Programming
by Manuel Rubio-SanchezRecursion is one of the most fundamental concepts in computer science and a key programming technique that allows computations to be carried out repeatedly. Despite the importance of recursion for algorithm design, most programming books do not cover the topic in detail, despite the fact that numerous computer programming professors and researchers in the field of computer science education agree that recursion is difficult for novice students. Introduction to Recursive Programming provides a detailed and comprehensive introduction to recursion. This text will serve as a useful guide for anyone who wants to learn how to think and program recursively, by analyzing a wide variety of computational problems of diverse difficulty. It contains specific chapters on the most common types of recursion (linear, tail, and multiple), as well as on algorithm design paradigms in which recursion is prevalent (divide and conquer, and backtracking). Therefore, it can be used in introductory programming courses, and in more advanced classes on algorithm design. The book also covers lower-level topics related to iteration and program execution, and includes a rich chapter on the theoretical analysis of the computational cost of recursive programs, offering readers the possibility to learn some basic mathematics along the way. It also incorporates several elements aimed at helping students master the material. First, it contains a larger collection of simple problems in order to provide a solid foundation of the core concepts, before diving into more complex material. In addition, one of the book's main assets is the use of a step-by-step methodology, together with specially designed diagrams, for guiding and illustrating the process of developing recursive algorithms. Furthermore, the book covers combinatorial problems and mutual recursion. These topics can broaden students' understanding of recursion by forcing them to apply the learned concepts differently, or in a more sophisticated manner. The code examples have been written in Python 3, but should be straightforward to understand for students with experience in other programming languages. Finally, worked out solutions to over 120 end-of-chapter exercises are available for instructors.
Introduction to Reliable and Secure Distributed Programming
by Christian Cachin Luís Rodrigues Rachid GuerraouiThe scope of this second edition of the introduction to fundamental distributed programming abstractions has been extended to cover 'Byzantine fault tolerance'. It includes algorithms to implement these abstractions in vulnerable distributed systems.
Introduction to Responsible AI: Implement Ethical AI Using Python
by Avinash Manure Shaleen Bengani Saravanan SLearn and implement responsible AI models using Python. This book will teach you how to balance ethical challenges with opportunities in artificial intelligence. The book starts with an introduction to the fundamentals of AI, with special emphasis given to the key principles of responsible AI. The authors then walk you through the critical issues of detecting and mitigating bias, making AI decisions understandable, preserving privacy, ensuring security, and designing robust models. Along the way, you’ll gain an overview of tools, techniques, and code examples to implement the key principles you learn in real-world scenarios. The book concludes with a chapter devoted to fostering a deeper understanding of responsible AI’s profound implications for the future. Each chapter offers a hands-on approach, enriched with practical insights and code snippets, enabling you to translate ethical considerations into actionable solutions. What You Will Learn Understand the principles of responsible AI and their importance in today's digital worldMaster techniques to detect and mitigate bias in AIExplore methods and tools for achieving transparency and explainabilityDiscover best practices for privacy preservation and security in AIGain insights into designing robust and reliable AI models Who This Book Is For AI practitioners, data scientists, machine learning engineers, researchers, policymakers, and students interested in the ethical aspects of AI
Introduction to Reversible Computing (Chapman & Hall/CRC Computational Science #19)
by Kalyan S. PerumallaCollecting scattered knowledge into one coherent account, this book provides a compendium of both classical and recently developed results on reversible computing. It offers an expanded view of the field that includes the traditional energy-motivated hardware viewpoint as well as the emerging application-motivated software approach. It explores up-and-coming theories, techniques, and tools for the application of reversible computing. The topics covered span several areas of computer science, including high-performance computing, parallel/distributed systems, computational theory, compilers, power-aware computing, and supercomputing.
Introduction to Scheduling (Chapman & Hall/CRC Computational Science)
by Yves Robert Frédéric VivienFull of practical examples, Introduction to Scheduling presents the basic concepts and methods, fundamental results, and recent developments of scheduling theory. With contributions from highly respected experts, it provides self-contained, easy-to-follow, yet rigorous presentations of the material.The book first classifies scheduling problems and
Introduction to Scientific and Technical Computing
by FRANK T. WILLMORE, ERIC JANKOWSKI AND CORAY COLINACreated to help scientists and engineers write computer code, this practical book addresses the important tools and techniques that are necessary for scientific computing, but which are not yet commonplace in science and engineering curricula. This book contains chapters summarizing the most important topics that computational researchers need to know about. It leverages the viewpoints of passionate experts involved with scientific computing courses around the globe and aims to be a starting point for new computational scientists and a reference for the experienced. Each contributed chapter focuses on a specific tool or skill, providing the content needed to provide a working knowledge of the topic in about one day. While many individual books on specific computing topics exist, none is explicitly focused on getting technical professionals and students up and running immediately across a variety of computational areas.